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Data lake consulting
We design data lakes that keep raw data cheap to store but still easy to find, trust and query, so analysts and data scientists stop copying extracts around.
Storage layout, file formats, partitioning and naming conventions agreed up front.
Batch and streaming loads from applications, SaaS tools and files.
Data catalogue, ownership, classification and fine-grained permissions.
Engines such as Athena, Synapse, BigQuery or Databricks SQL for analysts.
A lake becomes a swamp when nobody knows what a dataset means or whether it is current. We attach an owner, a description and a freshness check to each curated dataset, and we keep raw zones out of reach of casual reporting.
Step 1
We agree the decisions the data should support and the measures that matter.
Step 2
We profile your sources, note quality issues and confirm what is feasible.
Step 3
Pipelines, models and reports are delivered one subject area at a time.
Step 4
Documentation, access controls and training so your team can run and extend it.
Many organisations need both, or a lakehouse that combines them. We recommend based on your data types, users and budget.
Yes. We usually build on your existing cloud, warehouse and BI licences, and recommend new tools only when there is a clear gap.
Share your goals and current setup. We will come back with questions, a suggested approach and the next steps.
Contact Raushang4 Technology